The amount of available content items, such as videos, articles, music files, products that can be ordered, blogs etc., increase day by day, such that a user might encounter severe difficulties in finding content items that are of actual interest to him/her. The user may, thus, end up spending a lot of time in browsing and searching for relevant content items.
There are two popular ways of finding content items, namely conventional keyword-based search on the one side and browsing through a content item database by similarity or by relatedness on the other side. In a keyword-based search, a user specifies a query by entering one or more keywords in a search engine and is provided with a list of results matching somehow to the entered keywords. This way of finding content items is suitable if a user has a fairly precise idea of what he/she is looking for.
If, in the other case, a user has not a specific idea of what he/she is looking for, or, respectively, if he/she has already found something of interest and wants to explore the content item database for related or similar content items, then browsing by similarity or by relatedness is performed. For instance, if a user selects one of a plurality of displayed content items on a website of a certain content item database, the website can be organized such that, after selection of the certain content item, the website displays further content items that are similar or related to the selected content item.
In an example, a user browses through a video database, such as YOUTUBE.COM, and watches a video about a FORMULA 1 race. The video database website is often organized such that miniature graphical representations of similar/related videos are displayed in a display area, wherein the user can select one of these similar/related videos for viewing. Many websites of content item databases are organized in a similar manner, e.g., AMAZON.COM or any other website of an online shop.
Some methods of computing a measure of similarity or relatedness between two content items are known. For instance, some approaches are based on metadata about content items, and others are based on co-visitation counts, wherein, e.g., a number of times a user watches two videos in a same viewing session is counted.
A principle of gathering related content items shall now be explained with respect to FIG. 1, wherein the content item type shall be, in this example, a video. For each pair of videos (vi, vi1 . . . iN), a relatedness score r(vi, vi1 . . . iN) can be computed. Each video vi has an associated set of related videos Ri containing top-N videos ranked by their relatedness score r(vi, vi1 . . . iN). The related videos can be seen as inducing a directed graph over the set of videos. Two videos vi and vi1 . . . iN are connected by an edge ei1 . . . iN, if vi1 . . . iN belongs to the set Ri, with the weight of the edge given by the relatedness score r(vi, vi1 . . . iN).
When browsing by similarity or by relatedness, the user is presented with a list of content items that are related to a selected content item. When selecting another content item from the list of related content items, a new list of related content items is shown, thereby allowing further search for related content items.
United States patent application US 2008/0097975 A1 describes a simulation-assisted search technique. A visually-oriented search system guides a search with non-verbal inputs. Instead of specifying discrete attributes (words) as input to a search engine, a user may create a visual model of a desired end result and apply the model as a generalized input from which discrete attributes are extracted for submission to conventional search engines. The search can apparently be enhanced with a simulation of the visually-created query, and the simulation may be transformed into a query suitable for distribution to one or more search engines. The query may be refined using domain specific rules, vocabulary, expert systems, and the like. Search results may be browsed by a user, or employed to further refine subsequent searches.
United States patent application US 2006/0155684 A1 discloses a method of presenting web image search results for effective image browsing. So-called task-based attention objects for each of multiple images associated with image search results are generated. Thumbnail images from respective ones of the images are created as a function of at least the task-based attention objects. The thumbnail images shall emphasize image region(s) of greater priority to a user in view of a keyword or expanded keyword.
Given the large amount of available content items, the list of related or similar content items being displayed when browsing through a content item database can be rather long and may, thus, not provide a good overview to the user.